## Machine Learning Regression Masterclass in Python

### What you’ll learn

Master Python programming and Scikit learn as applied to machine learning regression

Understand the underlying theory behind simple and multiple linear regression techniques

Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy

Apply multiple linear regression to predict stock prices and Universities acceptance rate

Cover the basics and underlying theory of polynomial regression

Apply polynomial regression to predict employees’ salary and commodity prices

Understand the theory behind logistic regression

Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features

Understand the underlying theory and mathematics behind Artificial Neural Networks

Learn how to train network weights and biases and select the proper transfer functions

Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods

Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance

Apply ANNs to predict house prices given parameters such as area, number of rooms..etc

Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test

Understand the underlying theory and intuition behind Lasso and Ridge regression techniques

Sample real-world, practical projects

### Requirements

Machine Learning basics

PC with Internet connetion

### Description

Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries.Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including:· Simple Linear Regression· Multiple Linear Regression· Polynomial Regression· Logistic Regression· Decision trees regression· Ridge Regression· Lasso Regression· Artificial Neural Networks for Regression analysis· Regression Key performance indicatorsThe course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.

### Overview

Section 1: INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]

Lecture 1 Course Welcome Message

Lecture 2 Updates on Udemy Reviews

Lecture 3 Course Overview

Lecture 4 BONUS: Learning Path

Lecture 5 ML vs. DL vs. AI

Lecture 6 Get the materials

Section 2: ANACONDA AND JUPYTER INSTALLATION

Lecture 7 Download and Set up Anaconda

Lecture 8 What is Jupiter Notebook

Section 3: SIMPLE LINEAR REGRESSION

Lecture 9 Intro to Simple Linear Regression

Lecture 10 Simple Linear Regression Intuition

Lecture 11 Least Squares

Lecture 12 Project #1 – Overview

Lecture 13 Project #1 – Data Visualization

Lecture 14 Project #1 – Divide Data into Training and Testing

Lecture 15 Project #1 – Train Model

Lecture 16 Project #1 – Test Model

Lecture 17 Project #2 – Overview

Lecture 18 Project #2 – Solution

Lecture 19 Project #2 – Visualization

Lecture 20 Project #2 – Prepare Training and Testing Data

Lecture 21 Project #2 – Test Model

Lecture 22 Project #2 – Model Testing

Section 4: REGRESSION KEY PERFORMANCE INDICATORS

Lecture 23 Regression Metrics Intro

Lecture 24 Regression Metric Part 1

Lecture 25 Regression Metric Part 2

Lecture 26 Bias Variance Tradeoff

Section 5: POLYNOMIAL REGRESSION

Lecture 27 Polynomial Regression Intro

Lecture 28 Polynomial Regression – Intuition

Lecture 29 Poly Regression – Salary Load Data

Lecture 30 Poly Regression – Visualize Data

Lecture 31 Poly Regression – Linear Trainingtesting

Lecture 32 Poly Regression – Poly Part 1

Lecture 33 Poly Regression – Poly Part 2

Lecture 34 Poly Regression Project 2 Overview

Lecture 35 Poly Regression – Economies Linear -1

Lecture 36 Poly Regression – Economies Linear -2

Lecture 37 Poly Regression – Economies Poly

Section 6: MULTIPLE LINEAR REGRESSION

Lecture 38 Multiple Linear Regression Intro

Lecture 39 Multiple Linear Regression Overview

Lecture 40 Project #1 – Load Data and Libraries

Lecture 41 Project #1 – Data Visualization

Lecture 42 Project #1 – Model Training and Evaluation

Lecture 43 Project #1 – Model Results Evaluation

Lecture 44 Project #2 – Overview

Lecture 45 Project #2 – Load Data

Lecture 46 Project #2 – Data Visualization

Lecture 47 Project #2 – Train the Model

Lecture 48 Project #2 – Model Evaluation

Lecture 49 Project #2 – Retraining Model

Section 7: LOGISTIC REGRESSION

Lecture 50 Logistic Regression Intro

Lecture 51 Logistic Regression Intuition

Lecture 52 Confusion Matrix

Lecture 53 Project #2 – Data Import

Lecture 54 Project #2 – Visualization

Lecture 55 Project #2 – Data Cleaning

Lecture 56 Project #2 – Training Testing

Lecture 57 Model Testing Visualization

Section 8: APPLY ARTIFICIAL NEURAL NETWORKS TO PERFORM REGRESSION TASKS

Lecture 58 Artificial Neural Networks Intro

Lecture 59 Theory Part 1

Lecture 60 Theory Part 2

Lecture 61 Theory Part 3

Lecture 62 Theory Part 4

Lecture 63 Theory Part 5

Lecture 64 Theory Part 6

Lecture 65 Project – Load Dataset

Lecture 66 Project – Visualize Dataset

Lecture 67 Scale the Data

Lecture 68 Train the Model

Lecture 69 Evaluate the Model

Lecture 70 Multiple Linear regression

Lecture 71 Model Improvement with more features

Section 9: LASSO AND RIDGE REGRESSION

Lecture 72 Ridge and Lasso Intro

Lecture 73 Ridge Lasso Part 1

Lecture 74 Ridge Lasso Part 2

Lecture 75 Ridge Lasso Part 3

Lecture 76 Ridge and Lasso in Practice

Section 10: Bonus Lectures

Lecture 77 ***YOUR SPECIAL BONUS***

Data Scientists who want to apply their knowledge on Real World Case Studies,Machine Learning Enthusiasts who look to add more projects to their Portfolio

#### Course Information:

Udemy | English | 10h 21m | 5.11 GB

Created by: Dr. Ryan Ahmed, Ph.D., MBA

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